Develop Effective Immunogenicity Risk Mitigation Strategies Immunogenicity assessment is a prerequisite for the successful development of biopharmaceuticals, including safety and efficacy evaluation. Using advanced statistical methods in the study design and analysis stages is therefore essential to immunogenicity risk assessment and mitigation strategies. Statistical Methods for Immunogenicity Assessment provides a single source of information on statistical concepts, principles, methods, and strategies for detection, quantification, assessment, and control of immunogenicity. The book first gives an overview of the impact of immunogenicity on biopharmaceutical development, regulatory requirements, and statistical methods and strategies used for immunogenicity detection, quantification, and risk assessment and mitigation. It then covers anti-drug antibody (ADA) assay development, optimization, validation, and transfer as well as the analysis of cut point, a key assay performance parameter in ADA assay development and validation.
The authors illustrate how to apply statistical modeling approaches to establish associations between ADA and clinical outcomes, predict immunogenicity risk, and develop risk mitigation strategies. They also present various strategies for immunogenicity risk control. The book concludes with an explanation of the computer codes and algorithms of the statistical methods. A critical issue in the development of biologics, immunogenicity can cause early termination or limited use of the products if not managed well. This book shows how to use robust statistical methods for detecting, quantifying, assessing, and mitigating immunogenicity risk. It is an invaluable resource for anyone involved in immunogenicity risk assessment and control in both non-clinical and clinical biopharmaceutical development.
Introduction Background Immunogenicity Impact of Immunogenicity Regulatory Environment and Guidelines Statistics in Immunogenicity Risk Assessment Statistical Considerations in Comparative Immunogenicity Studies Concluding Remarks ADA Assay Development and Validation ADA Assays Assay Development and Validation Design of Experiment Method Transfer Determination of ADA Assay Cut Point Introduction Cut Point Experimental Design Statistical Methods for Cut Point Determination Clinical Immunogenicity Assessment Introduction Monoclonal Antibodies for the Treatment of Rheumatoid Arthritis (RA) Statistical Analysis of ADA Status Effects of ADA on Drug Efficacy Effect of ADA on AEs Relationship between ADA and Pharmacokinetics Statistical Analysis ADA Onset and Duration Statistical Analysis of ADA Titer Meta-Analysis Immunogenicity Risk Control Introduction Risk Assessment Immunogenicity Risk Control Biomarkers for Immunogenicity Concluding Remarks Computational Tools for Immunogenicity Analysis Read Data into R ADA Assay and Cut Point Implementation of Statistical Analysis of Clinical Immunogenicity Assessment Graphical Tools for Cause and Effect and Design Space Analysis Immunogenicity Biomarker Discovery Report Automation Bibliography Index